Over at the Climate Dialogue website we start with what could become a very interesting discussion about the so-called tropical hot spot. Climate models show amplified warming high in the tropical troposphere due to greenhouse forcing. However data from satellites and weather balloons don’t show much amplification. What to make of this? Have the models been ‘falsified’ as critics say or are the errors in the data so large that we cannot conclude much at all? And does it matter if there is no hot spot?
The (missing) tropical hot spot is one of the long-standing controversies in climate science. In 2008 two papers were published, one by a few scientists critical of the IPCC view (Douglass, Christy, Pearson and Singer) and one by Ben Santer and sixteen other scientists. We have participants from both papers. John Christy is the ‘representative’ from the first paper and Steven Sherwood and Carl Mears are ‘representatives’ of the second paper.
Below I repost the introduction that we – the editors of Climate Dialogue – prepared as the basis for the discussion. Feel free to post it on your own blog with a link to the discussion at climatedialogue.org.
The (missing) hot spot in the tropics
Based on theoretical considerations and simulations with General Circulation Models (GCMs), it is expected that any warming at the surface will be amplified in the upper troposphere. The reason for this is quite simple. More warming at the surface means more evaporation and more convection. Higher in the troposphere the (extra) water vapour condenses and heat is released. Calculations with GCMs show that the lower troposphere warms about 1.2 times faster than the surface. For the tropics, where most of the moist is, the amplification is larger, about 1.4.
This change in thermal structure of the troposphere is known as the lapse rate feedback. It is a negative feedback, i.e. attenuating the surface temperature response due to whatever cause, since the additional condensation heat in the upper air results in more radiative heat loss.
IPCC published the following figure in its latest report (AR4) in 2007:
Source: http://www.ipcc.ch/publications_and_data/ar4/wg1/en/figure-9-1.html (based on Santer 2003)
The figure shows the response of the atmosphere to different forcings in a GCM. As one can see, over the past century, the greenhouse forcing was expected to dominate all other forcings. The expected warming is highest in the tropical troposphere, dubbed the tropical hot spot.
The discrepancy between the strength of the hot spot in the models and the observations has been a controversial topic in climate science for almost 25 years. The controversy [i] goes all the way back to the first paper of Roy Spencer and John Christy [ii] about their UAH tropospheric temperature dataset in the early nineties. At the time their data didn’t show warming of the troposphere. Later a second group (Carl Mears and Frank Wentz of RSS) joined in, using the same satellite data to convert them into a time series of the tropospheric temperature. Several corrections, e.g. for the orbital changes of the satellite, were made in the course of years with a warming trend as a result. However the controversy remains because the tropical troposphere is still showing a smaller amplification of the surface warming which is contrary to expectations.
Some researchers claim that observations don’t show the tropical hot spot and that the differences between models and observations are statistically significant [iii]. On top of that they note that the warming trend itself is much larger in the models than in the observations (see figure 2 below and also ref. [iv]). Other researchers conclude that the differences between the trends of tropical tropospheric temperatures in observations and models are statistically not inconsistent with each other [v]. They note that some radiosonde and satellite datasets (RSS) do show warming trends comparable with the models (see figure 3 below).
The debate is complex because there are several observational datasets, based on satellite (UAH and RSS) but also on radiosonde measurements (weather balloons). Which of the dataset is “best” and how does one determine the uncertainty in both datasets and model simulations?
The controversy flared up in 2007/2008 with the publications of two papers [vi][vii] of the opposing groups. Key graphs in both papers are the best way to give an impression of the debate. First Douglass et al. came up with the following graph showing the disagreement between models and observations:
Figure 2. Temperature trends for the satellite era. Plot of temperature trend (°C/decade) against pressure (altitude). The HadCRUT2v surface trend value is a large blue circle. The GHCN and the GISS surface values are the open rectangle and diamond. The four radiosonde results (IGRA, RATPAC, HadAT2, and RAOBCORE) are shown in blue, light blue, green, and purple respectively. The two UAH MSU data points are shown as gold-filled diamonds and the RSS MSU data points as gold-filled squares. The 22-model ensemble average is a solid red line. The 22-model average ±2σSE are shown as lighter red lines. MSU values of T2LT and T2 are shown in the panel to the right. UAH values are yellow-filled diamonds, RSS are yellow-filled squares, and UMD is a yellow-filled circle. Synthetic model values are shown as white-filled circles, with 2σSE uncertainty limits as error bars. Source: Douglass et al. 2008
Santer et al. criticized Douglass et al. for underestimating the uncertainties in both model output and observations and also for not showing all radiosonde datasets. They came up with the following graph:
Figure 3. Vertical profiles of trends in atmospheric temperature (panel A) and in actual and synthetic MSU temperatures (panel B). All trends were calculated using monthly-mean anomaly data, spatially averaged over 20 °N–20 °S. Results in panel A are from seven radiosonde datasets (RATPAC-A, RICH, HadAT2, IUK, and three versions of RAOBCORE; see Section 2.1.2) and 19 different climate models. The grey-shaded envelope is the 2σ standard deviation of the ensemble-mean trends at discrete pressure levels. The yellow envelope represents 2σSE, DCPS07’s estimate of uncertainty in the mean trend. The analysis period is January 1979 through December 1999, the period of maximum overlap between the observations and most of the model 20CEN simulations. Note that DCPS07 used the same analysis period for model data, but calculated all observed trends over 1979–2004. Source: Santer (2008)
The grey-shaded envelope is the 2σ standard deviation of the ensemble-mean trends of Santer et al. while the yellow band is the estimated uncertainty of Douglass et al. Some radiosonde series in the Santer graph (like the Raobcore 1.4 dataset) show even more warming higher up in the troposphere than the model mean.
Not surprisingly the debate didn’t end there. In 2010 McKitrick et al. [viii] updated the results of Santer (2008), who limited the comparison between models and observations to the period 1979-1999, to 2009. They concluded that over the interval 1979–2009, model projected temperature trends are two to four times larger than observed trends in both the lower troposphere and the mid troposphere and the differences are statistically significant at the 99% level.
Christy (2010)[ix] analysed the different datasets used and concluded that some should be discarded in the tropics:
Figure 4. Temperature trends in the lower tropical troposphere for different datasets and for slightly differing periods (79-05 = 1979-2005). UAH and RSS are the estimates based on satellite measurements. HadAt, Ratpac, RC1.4 and Rich are based on radiosonde measurements. C10 and AS08 [x] are based on thermal wind data. The other three datasets give trends at the surface (ERSST being for the oceans only while the other two combine land and ocean data). Source: Christy (2010)
Christy (2010) concluded that part of the tropical warming in the RSS series is spurious. They also discarded the indirect estimates that are based on thermal wind. Not surprisingly Mears (2012) disagreed with Christy’s conclusion about the RSS trend being spurious writing that “trying to determine which MSU [satellite] data set is “better” based on short-time period comparisons with radiosonde data sets alone cannot lead to robust conclusions”.[xi]
Christy (2010) also introduced what they called the “scaling ratio”, the ratio of tropospheric to surface trends and concluded that these scaling ratios clearly differ between models and observations. Models show a ratio of 1.4 in the tropics (meaning troposphere warming 1.4 times faster than the surface), while the observations have a ratio of 0.8 (meaning surface warming faster than the troposphere). Christy speculated that an alternate reason for the discrepancy could be that the reported trends in temperatures at the surface are spatially inaccurate and are actually less positive. A similar hypothesis was tested by Klotzbach (2009).[xii]
In an extensive review article about the controversy published in early 2011 Thorne et al. ended with the conclusion that “there is no reasonable evidence of a fundamental disagreement between tropospheric temperature trends from models and observations when uncertainties in both are treated comprehensively”. However in the same year Fu et al.[xiii] concluded that while “satellite MSU/AMSU observations generally support GCM results with tropical deep‐layer tropospheric warming faster than surface, it is evident that the AR4 GCMs exaggerate the increase in static stability between tropical middle and upper troposphere during the last three decades”. More papers then started to acknowledge that the consistency of tropical tropospheric temperature trends with climate model expectations remains contentious.[xiv][xv][xvi][xvii]
We will focus the discussion on the tropics as the hot spot is most pronounced there in the models. Core questions are of course whether we can detect/have detected a hot spot in the observations and if not what are the implications for the reliability of GCMs and our understanding of the climate?
1) Do the discussants agree that amplified warming in the tropical troposphere is expected?
2) Can the hot spot in the tropics be regarded as a fingerprint of greenhouse warming?
3) Is there a significant difference between modelled and observed amplification of surface trends in the tropical troposphere (as diagnosed by e.g. the scaling ratio)?
4) What could explain the relatively large difference in tropical trends between the UAH and the RSS dataset?
5) What explanation(s) do you favour regarding the apparent discrepancy surrounding the tropical hot spot? A few options come to mind: a) satellite data show too little warming b) surface data show too much warming c) within the uncertainties of both there is no significant discrepancy d) the theory (of moist convection leading to more tropospheric than surface warming) overestimates the magnitude of the hotspot
6) What consequences, if any, would your explanation have for our estimate of the lapse rate feedback, water vapour feedback and climate sensitivity?
[i] Thorne, P. W. et al., 2011, Tropospheric temperature trends: History ofan ongoing controversy. WIRES: Climate Change, 2: 66-88
[ii]Spencer RW, Christy JR. Precise monitoring of global temperature trends from satellites. Science 1990, 247:1558–1562.
[iii] Christy, J. R., B. M. Herman, R. Pielke Sr., P. Klotzbach, R. T. McNider, J. J. Hnilo, R. W. Spencer, T. Chase, and D. H. Douglass (2010), What do observational datasets say about modeled tropospheric temperature trends since 1979?, Remote Sens., 2, 2148–2169, doi:10.3390/rs2092148.
[v]Thorne, P.W. Atmospheric science: The answer is blowing in the wind. Nature Geosci. 2008, doi:10.1038/ngeo209
[vi] Douglass DH, Christy JR, Pearson BD, Singer SF. A comparison of tropical temperature trends with model predictions. Int J Climatol 2008, 27:1693–1701
[vii] Santer, B.D.; Thorne, P.W.; Haimberger, L.; Taylor, K.E.; Wigley, T.M.L.; Lanzante, J.R.; Solomon, S.; Free, M.; Gleckler, P.J.; Jones, P.D.; Karl, T.R.; Klein, S.A.; Mears, C.; Nychka, D.; Schmidt, G.A.; Sherwood, S.C.; Wentz, F.J. Consistency of modelled and observed temperature trends in the tropical troposphere. Int. J. Climatol. 2008, doi:1002/joc.1756
[viii] McKitrick, R. R., S. McIntyre and C. Herman (2010) “Panel and Multivariate Methods for Tests of Trend Equivalence in Climate Data Sets.” Atmospheric Science Letters, 11(4) pp. 270-277, October/December 2010 DOI: 10.1002/asl.290
[ix] Christy, J. R., B. M. Herman, R. Pielke Sr., P. Klotzbach, R. T. McNider, J. J. Hnilo, R. W. Spencer, T. Chase, and D. H. Douglass (2010), What do observational datasets say about modeled tropospheric temperature trends since 1979?, Remote Sens., 2, 2148–2169, doi:10.3390/rs2092148
[x] Allen RJ, Sherwood SC. Warming maximum in the tropical upper troposphere deduced from thermal winds. Nat Geosci 008, 1:399–403
[xi] Mears, C. A., F. J. Wentz, and P. W. Thorne (2012), Assessing the value of Microwave Sounding Unit–radiosonde comparisons in ascertaining errors in climate data records of tropospheric temperatures, J. Geophys. Res., 117, D19103, doi:10.1029/2012JD017710
[xii] Klotzbach PJ, Pielke RA Sr., Pielke RA Jr., Christy JR, McNider RT. An alternative explanation for differential temperature trends at the surface and in the lower troposphere. J Geophys Res 2009, 114:D21102. DOI:10.1029/2009JD011841
[xiii] Fu, Q., S. Manabe, and C. M. Johanson (2011), On the warming in the tropical upper troposphere: Models versus observations, Geophys. Res. Lett., 38, L15704, doi:10.1029/2011GL048101
[xiv] Seidel, D. J., M. Free, and J. S. Wang (2012), Reexamining the warming in the tropical upper troposphere: Models versus radiosonde observations, Geophys. Res. Lett., 39, L22701, doi:10.1029/2012GL053850
[xv] Po-Chedley, S., and Q. Fu (2012), Discrepancies in tropical upper tropospheric warming between atmospheric circulation models and satellites, Environ. Res. Lett
[xvi] Benjamin D. Santer, Jeffrey F. Painter, Carl A. Mears, Charles Doutriaux, Peter Caldwell, Julie M. Arblaster, Philip J. Cameron-Smith, Nathan P. Gillett, Peter J. Gleckler, John Lanzante, Judith Perlwitz, Susan Solomon, Peter A. Stott, Karl E. Taylor, Laurent Terray, Peter W. Thorne, Michael F. Wehner, Frank J. Wentz, Tom M. L. Wigley, Laura J. Wilcox, and Cheng-Zhi Zou, Identifying human influences on atmospheric temperature, PNAS 2013 110 (1) 26-33; published ahead of print November 29, 2012, doi:10.1073/pnas.1210514109
[xvii] Thorne, P. W., et al. (2011), A quantification of uncertainties in historical tropical tropospheric temperature trends from radiosondes, J. Geophys. Res., 116, D12116, doi:10.1029/2010JD015487